Systems and methods for data collection and analysis at the edge

ABSTRACT

A microelectronic device for generating analysis results from data received from a sensor.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.16/275,305, filed on 13 Feb. 2019, which claims the benefit of U.S.Provisional Application number 62/630,802, filed on 14 Feb. 2018, andU.S. Provisional Application number 62/683,497, filed on 11 Jun. 2018,which are incorporated in their entirety by this reference.

TECHNICAL FIELD

This disclosure relates generally to data collection at edge devices,and more specifically to new and useful systems and methods foranalyzing data at an edge device.

BACKGROUND

Machine learning applications typically involve use of purpose-builthardware that analyzes data collected from one or more separate hardwaredevices. This is typically accomplished in a manner where the datacollection process is disjoint from the analysis and learning processes.Moreover, the analysis and learning processes are typically accomplishedin systems that are purpose-built complex arrangements of high-costcomputing hardware and software infrastructure. While such solutionsmight be effective at applying learning algorithms to data, importantanalysis, assessment and control capabilities are sometimes missing.These missing capabilities might prevent current solutions from beingwell suited for applying learning and analysis techniques to data as itis collected at the edge where this information is created. Due to thedisjoint nature of data collection, analysis and learning inconventional systems, the data to which algorithms are applied is“stale” at the time of consumption. As a result conventional systemsmight have limited applicability to real-time data.

BRIEF DESCRIPTION OF THE FIGURES

FIG. 1 is a schematic representation of a system, according toembodiments;

FIG. 2 is a schematic representation of a system, according toembodiments;

FIG. 3 is a schematic representation of a system, according toembodiments;

FIG. 4 is a schematic representation of a system, according toembodiments;

FIG. 5 is a representations of a method, according to embodiments;

FIGS. 6A-B is a representations of a method, according to embodiments;and

FIGS. 7-14 are schematic representations of systems, according toembodiments.

DESCRIPTION OF EMBODIMENTS

The following description of embodiments is not intended to limit thedisclosure to these embodiments, but rather to enable any person skilledin the art to make and use the embodiments disclosed herein.

1. Overview

Embodiments described herein provide systems and methods for collectionand processing of data at an edge via a microelectronic device thatincludes a sensor and a compute fabric. In some embodiments, data iscollected and processed by using a microelectronics device that includesone or more sensors and one or more compute fabric components (e.g.,data processing components, data storage components), wherein thesensors are electrically or communicatively coupled to the computefabric components.

In some embodiments, the one or more sensors and one or more computefabric components are included in a same microelectronic device package.

In some embodiments, at least one sensor is integrated into a computefabric of the microelectronics device, wherein the compute fabricincludes the one or more compute fabric components.

In some embodiments, at least one sensor is fabricated in a firstsemiconductor integrated circuit die, the one or more compute fabriccomponents are fabricated in a second semiconductor integrated circuitdie, and at least one sensor of the first integrated circuit die isdirectly coupled to at least one compute fabric component of the secondsemiconductor integrated circuit die via an interface medium.

In some embodiments, at least one sensor is fabricated in a firstsemiconductor integrated circuit die, the one or more compute fabriccomponents are fabricated in a second semiconductor integrated circuitdie, at least one sensor of the first integrated circuit die is directlycoupled to at least one compute fabric component of the secondsemiconductor integrated circuit die via an interface medium, and asensor external to the microelectronic device is communicatively coupled(or electrically coupled) to a sensor of the first semiconductorintegrated circuit die.

In some embodiments, a sensor external to the compute fabric iscommunicatively coupled (or electrically coupled) to the compute fabricvia a bridge interface medium that is external to the microelectronicdevice, and the bridge medium is communicatively (or electrically)coupled to the compute fabric.

In some embodiments, a sensor external to the compute fabric iselectrically coupled to the compute fabric via an electric interconnect.

In some embodiments, a sensor external to the compute fabric iselectrically coupled to the compute fabric via another sensor that iscoupled to the compute fabric.

In some embodiments, the compute fabric receives sensor data from adevice that is external to the compute fabric and that is electricallycoupled to the compute fabric compute fabric via an electricinterconnect.

In some embodiments, the compute fabric receives sensor data from adevice that is external to the compute fabric and that is electricallycoupled to the compute fabric via another sensor that is coupled to thecompute fabric.

In some embodiments, the compute fabric includes at least one sensorthat is constructed to receive sensor data transmitted by an externaltransmitter that is communicatively coupled to a sensor that is externalto the compute fabric, wherein the sensor that is external to thecompute fabric generates the sensor data transmitted by the externaltransmitter.

In some embodiments, the compute fabric is constructed to selectivelyenable collection of data from one or more sensor components of themicroelectronic device.

In some embodiments, the compute fabric of the microelectronic device isconstructed to combine data captured by two or more sensor components ofthe microelectronic device.

In some embodiments, the compute fabric of the microelectronic device isconstructed to preprocess data collected by one or more sensorcomponents of the microelectronics device by transforming the data intoa format for machine learning processing by the compute fabric.

In some embodiments, the compute fabric of the microelectronic device isconstructed to perform a machine learning process.

In some embodiments, the compute fabric of the microelectronic device isconstructed to perform a statistical classification process. In someembodiments, the compute fabric of the microelectronic device isconstructed to perform a statistical spectral density estimation. Insome embodiments, the compute fabric of the microelectronic device isconstructed to perform a clustering process. In some embodiments, thecompute fabric of the microelectronic device is constructed to perform aprincipal component analysis process. In some embodiments, the computefabric of the microelectronic device is constructed to perform anindependent component analysis process. In some embodiments, the computefabric of the microelectronic device is constructed to perform asingular value decomposition process. In some embodiments, the computefabric of the microelectronic device is constructed to perform alearning classifier process. In some embodiments, the compute fabric ofthe microelectronic device is constructed to perform a kernel estimatorprocess.

In some embodiments, the compute fabric of the microelectronics deviceis constructed to collect identifying information unique to intrinsicphysical specificities of at least one component of the microelectronicsdevice. In some embodiments, the identifying information is a PhysicallyUnclonable Function (PUF). In some embodiments, the identifyinginformation is used to generate a Physically Unclonable Function (PUF).

In some embodiments, the intrinsic physical specificities of at leastone component are related to the manufacturing of the microelectronicsdevice, and are unique with respect to another similarly manufacturedmicroelectronics device. In some embodiments, the collected identifyinginformation relates to intrinsic specificities of a selected processingcomponent of the compute fabric. In some embodiments, the collectedidentifying information relates to intrinsic specificities of a selectedstorage component of the compute fabric. In some embodiments, thecollected identifying information relates to intrinsic specificities ofa selected sensor component of the microelectronics device. In someembodiments, a processing component of the compute fabric collects theidentifying information. In some embodiments, a selected processingcomponent of the compute fabric collects the identifying information. Insome embodiments, the compute fabric collects the identifyinginformation by selecting at least one component (e.g., processingcomponent, storage component, sensor) of the microelectronics device,changing biasing and control parameters of each selected component ofthe microelectronics device, and generating the identifying informationbased on results of the changing of the biasing and control parameters.

In some embodiments, the microelectronics device uses the identifyinginformation to calibrate individual sensor components. In someembodiments, the microelectronics device uses the identifyinginformation to calibrate groups of sensor components.

In some embodiments, the microelectronics device uses the identifyinginformation to generate a secret cryptographic key. In some embodiments,the microelectronics device uses the identifying information to generatea cryptographic private/public key pair. In some embodiments, themicroelectronics device uses the identifying information to generateauthentication information. In some embodiments, the microelectronicsdevice uses the identifying information to generate authorizationinformation. In some embodiments, the microelectronics device uses theidentifying information to generate a digital signature. In someembodiments, the microelectronics device uses the identifyinginformation to generate data tagging information for data collected bythe microelectronics device. In some embodiments, the microelectronicsdevice uses the identifying information to encrypt data. In someembodiments, the microelectronics device uses the identifyinginformation to decrypt data.

In some embodiments, the compute fabric of the microelectronic device isconstructed to perform a machine learning process, and themicroelectronics device uses the identifying information to encrypt datagenerated by the machine learning process.

In some embodiments, the compute fabric of the microelectronic device isconstructed to perform a machine learning process, and themicroelectronics device uses the identifying information to digitallysign data generated by the machine learning process.

2. Systems FIG. 1

FIG. 1 is a schematic representation of a system 100, according to someembodiments. In some embodiments, the system 100 includes at least onesensor 101 and a runtime-adaptable compute fabric 102. In someembodiments, the system 100 includes a runtime-adaptable compute fabric102 that includes at least one sensor (e.g., a sensor similar to sensor101). In some embodiments, the runtime-adaptable compute fabric 102 isincluded in a microelectronic device. In some embodiments, the sensor101 is included in a microelectronic device. In some embodiments, theruntime-adaptable compute fabric 102 and the sensor 101 are included indifferent microelectronic devices. In some embodiments, theruntime-adaptable compute fabric 102 and the sensor 101 are included asame microelectronic device.

In some embodiments, the runtime-adaptable compute fabric 102 includes aplurality of compute fabric components, including at least oneprogrammable data processing circuit component (e.g., 122) and at leastone data storage circuit component (e.g., 123). In some embodiments, theruntime-adaptable compute fabric 102 includes a plurality of computefabric components, including at least one programmable data processingcircuit component (e.g., 122), at least one data storage circuitcomponent (e.g., 123) and at least one sensor.

In some embodiments, the compute fabric components of 102 are arrangedon a single compute fabric die. In some embodiments, the compute fabriccomponents of 102 are arranged on a plurality of compute fabric dies. Insome embodiments, a programmable data processing circuit component 122is coupled to a data storage circuit component 123, and the data storagecircuit component includes instructions 124 that are executed by thedata processing circuit component 122. In some embodiments, theprogrammable data processing circuit component 122 is re-programmed byupdating the instructions 124. In some embodiments, a programmable dataprocessing circuit component 132 is coupled to a data storage circuitcomponent 133, and the data storage circuit component includesinstructions 134 that are executed by the data processing circuitcomponent 132. In some embodiments, the programmable data processingcircuit component 132 is re-programmed by updating the instructions 134.

In some embodiments, system 100 includes a plurality of sensors. In someembodiments, the plurality of sensors and one or more compute fabriccomponents of the runtime-adaptable compute fabric 102 are included in asame microelectronic device package.

In some embodiments, at least one sensor is integrated into theruntime-adaptable compute fabric 102, wherein the compute fabricincludes the one or more compute fabric components.

In some embodiments, a programmable data processing circuit component122 is coupled to a sensor included in the compute fabric 102. In someembodiments, a data storage circuit component 123 is coupled to a sensorincluded in the compute fabric 102.

In some embodiments, at least one sensor is fabricated in a firstsemiconductor integrated circuit die, the one or more compute fabriccomponents are fabricated in a second semiconductor integrated circuitdie, and at least one sensor of the first integrated circuit die isdirectly coupled to at least one compute fabric component of the secondsemiconductor integrated circuit die via an interface medium.

In some embodiments, at least one sensor is fabricated in a firstsemiconductor integrated circuit die, the one or more compute fabriccomponents are fabricated in a second semiconductor integrated circuitdie, at least one sensor of the first integrated circuit die is directlycoupled to at least one compute fabric component of the secondsemiconductor integrated circuit die via an interface medium, and asensor external to the microelectronic device is communicatively coupled(or electrically coupled) to a sensor of the first semiconductorintegrated circuit die.

In some embodiments, a sensor is communicatively coupled (orelectrically coupled) to at least one compute fabric component via abridge interface medium that is external to the one or more computefabric component, and the bridge medium is communicatively (orelectrically) coupled to the one or more compute fabric component.

In some embodiments, a sensor is electrically coupled to the computefabric via an electric interconnect.

In some embodiments, a sensor is electrically coupled to the computefabric via another sensor that is coupled to the compute fabric.

In some embodiments, the compute fabric receives sensor data from adevice that is external to the compute fabric and that is electricallycoupled to the compute fabric via an electric interconnect.

In some embodiments, the compute fabric receives sensor data from adevice that is external to the compute fabric and that is electricallycoupled to the compute fabric via another sensor that is coupled to thecompute fabric.

In some embodiments, the compute fabric is coupled to a first sensorthat is constructed to receive sensor data transmitted by an externaltransmitter that is communicatively coupled to a second sensor that isexternal to the compute fabric, wherein the second sensor that isexternal to the compute fabric generates the sensor data transmitted bythe external transmitter.

In some embodiments, a first programmable data processing circuitcomponent (e.g., 122) is coupled to a first data storage circuitcomponent (e.g., 123) and at least a second data storage circuitcomponent (e.g., 133).

In some embodiments, a first programmable data processing circuitcomponent (e.g., 122) is coupled to a first data storage circuitcomponent (e.g., 123) and at least a second programmable data processingcircuit component (e.g., 132).

In some embodiments, a first programmable data processing circuitcomponent (e.g., 122) is coupled to at least a second programmable dataprocessing circuit component (e.g., 132).

In some embodiments, a first programmable data processing circuitcomponent (e.g., 122) is coupled to a first data storage circuitcomponent (e.g., 123), and at least a second programmable dataprocessing circuit component (e.g., 132) is also coupled to the firstdata storage circuit component (e.g., 123).

In some embodiments, the system 100 includes a sensor constructed tomeasure voltage and a circuit constructed to measure current.

In some embodiments, the system 100 includes a sensor constructed tomeasure electromagnetic waves.

In some embodiments, the system 100 includes a sensor constructed tomeasure magnetic waves.

In some embodiments, the system 100 includes a sensor constructed tomeasure temperature.

FIG. 2

FIG. 2 is a schematic representation of a system 200 that is implementedas a microelectronic device that includes at least a first sensor die201 and a first runtime-adaptable compute fabric die 202. In someembodiments, the first sensor die 201 and the compute fabric die 202 areintegrated circuit semiconductor dies.

In some embodiments, the sensor die 201 includes a plurality of sensors(e.g., 211, 212, 213) including a first sensor 211. In some embodiments,the microelectronic device includes a plurality of sensor dies, eachsensor die including at least one sensor.

In some embodiments, the first runtime-adaptable compute fabric die 202includes a first programmable data processing circuit component 222 anda first data storage circuit component 223, wherein the firstprogrammable data processing circuit component is electrically coupledto the first data storage circuit component.

In some embodiments, the first runtime-adaptable compute fabric die 202includes a plurality of programmable data processing circuit components(e.g., 222, 232) and data storage circuit components (e.g., 223, 233),wherein within the first compute fabric die 202 at least one of theprogrammable data processing circuit components (e.g., 222) iselectrically coupled to at least one of the plurality of data storagecircuit components (e.g., 223).

In some embodiments, the microelectronic device includes a plurality ofruntime-adaptable compute fabric dies including the firstruntime-adaptable compute fabric die 202 and a second runtime-adaptablecompute fabric die 203. In some embodiments, each compute fabric dieincludes a first programmable data processing circuit component (e.g.,222, 242) and a first data storage circuit component (e.g., 223, 243),wherein the first programmable data processing circuit component iselectrically coupled to the first data storage circuit component. Insome embodiments, each compute fabric die (e.g., 202) includes aplurality of programmable data processing circuit components (e.g., 222,232, 242, 252) and data storage circuit components (e.g., 223, 233, 243,253), wherein within each compute fabric die (e.g., 202, 203) at leastone of the programmable data processing circuit components iselectrically coupled to at least one of the plurality of data storagecircuit components. In some embodiments, each data storage componentincludes instructions (e.g., 224, 234, 244, 254) that are executed by adata processing circuit component coupled to the data storage component.

In some embodiments, the microelectronic device includes at least onestorage component die 231, wherein each storage component die iselectrically coupled to at least one of the plurality of compute fabricdies (e.g., 202, 203). In some embodiments, the microelectronic deviceincludes at least one storage component die 231, wherein each storagecomponent die is electrically coupled to at least one of the pluralityof compute fabric dies (e.g., 202, 203) via one of an integratedinterface medium (as described herein), a bridge device (as describedherein), an electrical interconnect, and a transmitter (as describedherein).

In some embodiments, each sensor die (e.g., 201), compute fabric die(e.g., 202, 203), and storage component die (e.g., 231) is an integratedcircuit semiconductor die.

In some embodiments, the microelectronic device includes at least afirst compute fabric die (e.g., 202) and a second compute fabric die(e.g., 203) electrically coupled to the first compute fabric die (e.g.,202) via one of an integrated interface medium (as described herein), abridge device (as described herein), an electrical interconnect, and atransmitter (as described herein).

In some embodiments, a data processing component of the microelectronicdevice is electrically coupled to the first sensor 211. In someembodiments, a storage component of the microelectronic device iselectrically coupled to the first sensor 211.

In some embodiments, each compute fabric die has a same systemarchitecture. In some embodiments, each processing circuit component hasa same instruction set.

In some embodiments, at least one data processing circuit component(e.g., 222) is coupled to a data storage circuit component (e.g., 223)that includes processing circuit instructions (e.g., 224) for selectingat least one of a sensor (e.g., 211), a data storage circuit component(e.g., 222), and a data processing circuit component (e.g., 223) as anintrinsic properties component, and at least one data processing circuitcomponent is coupled to a data storage circuit component that includesprocessing circuit instructions (e.g., 234) for generating identifyinginformation by changing biasing and control parameters of the selectedintrinsic properties component, and generating the identifyinginformation based on the results of the changing of the biasing andcontrol parameters.

In some embodiments at least one storage component die includes a highbandwidth memory (HBM).

In some embodiments, at least one programmable data processing componentis constructed to perform linear algebra computation.

In some embodiments, at least one programmable data processing componentis constructed to perform arithmetic.

In some embodiments, at least a first compute fabric die is electricallycoupled to a second compute fabric die in a die stacking arrangement.

In some embodiments, at least a first compute fabric die is electricallyinterconnected to a second compute fabric die via at least one TSV, andan interposer die is stacked atop the first compute fabric die and thesecond compute fabric die.

In some embodiments, at least a first compute fabric die is electricallycoupled to a second compute fabric die via an interface medium. In someembodiments, the interface medium is a through-silicon via (TSV)vertical electrical connection. In some embodiments, the coupled diesare stacked to form a 3D integrated circuit. In some embodiments, aninterface medium involves a stacked 2.5D configuration were adjacent dieare interconnected using TSVs and an interposer die is stacked atop theadjacent die.

In some embodiments, a first compute fabric die is electrically coupledto a first storage component die in a die stacking arrangement.

In some embodiments, at least a first compute fabric die is electricallyinterconnected to a first storage component die via at least one TSV,and an interposer die is stacked atop the first compute fabric die andthe first storage component die.

In some embodiments, at least a first compute fabric die is electricallyinterconnected to a first storage component die via an interface medium.In some embodiments, the interface medium is a through-silicon via (TSV)vertical electrical connection. In some embodiments, the coupled diesare stacked to form a 3D integrated circuit. In some embodiments, aninterface medium involves a stacked 2.5D configuration were adjacent dieare interconnected using TSVs and an interposer die is stacked atop theadjacent die.

In some embodiments, at least a first storage component die iselectrically coupled to a second storage component die in a die stackingarrangement.

In some embodiments, at least a first storage component die iselectrically interconnected to a second storage component die via atleast one TSV, and an interposer die is stacked atop the first storagecomponent die and the second storage component die.

In some embodiments, at least a first storage component die iselectrically interconnected to a second storage component die via aninterface medium. In some embodiments, the interface medium is athrough-silicon via (TSV) vertical electrical connection. In someembodiments, the coupled dies are stacked to form a 3D integratedcircuit. In some embodiments, an interface medium involves a stacked2.5D configuration were adjacent die are interconnected using TSVs andan interposer die is stacked atop the adjacent die.

In some embodiments, each programmable data processing circuit componentis electrically coupled to at least one data storage circuit componentthat includes machine-executable program instructions that areexecutable by the programmable data processing circuit component, andwherein each programmable data processing circuit component isprogrammed by storing program instructions at the storage circuitcomponent electrically coupled to the data processing circuit component.

In some embodiments, the plurality of sensors are included in a firstsensor die, the first sensor die is an integrated circuit semiconductordie, and the first sensor die is electrically coupled to at least one ofa data processing component and a storage component of themicroelectronic device via one of an integrated interface medium and adie stacking arrangement.

In some embodiments, the integrated interface medium includesthrough-silicon via (TSV) vertical electrical connections.

In some embodiments, the first sensor die (e.g., 201) includes at leastone of a circuit constructed to measure voltage and a circuitconstructed to measure current.

In some embodiments, the first sensor die (e.g., 201) includes at leastone of a circuit constructed to measure electromagnetic waves.

In some embodiments, the first sensor die (e.g., 201) includes at leastone of a circuit constructed to measure magnetic waves.

In some embodiments, the first sensor die (e.g., 201) includes at leastone of a circuit constructed to measure temperature.

In some embodiments, the microelectronic device includes at least asecond sensor that is different from the first sensor.

In some embodiments, each programmable data processing circuit componenthas a same system architecture.

In some embodiments, a first programmable data processing circuitcomponent (e.g., 222) is coupled to a first data storage circuitcomponent (e.g., 223) and at least a second data storage circuitcomponent (e.g., 233, 243, 253).

In some embodiments, a first programmable data processing circuitcomponent (e.g., 222) is coupled to a first data storage circuitcomponent (e.g., 223) and at least a second programmable data processingcircuit component (e.g., 232, 242, 252).

In some embodiments, a first programmable data processing circuitcomponent (e.g., 222) is coupled to at least a second programmable dataprocessing circuit component (e.g., 232, 242, 252).

In some embodiments, a first programmable data processing circuitcomponent (e.g., 222) is coupled to a first data storage circuitcomponent (e.g., 223), and at least a second programmable dataprocessing circuit component (e.g., 232, 242, 252) is also coupled tothe first data storage circuit component (e.g., 223).

FIGS. 8-14

FIG. 8 is a schematic representation of a system Boo that includes acompute fabric die 801 that includes at least one sensor.

FIG. 9 is a schematic representation of a system 900 that includes acompute fabric die that includes at least one sensor, and a sensor diethat includes a plurality of sensors.

FIG. 10 is a schematic representation of a system 1000 that includesplural compute fabric dies that each include at least one sensor, asensor die that includes a plurality of sensors, and plural storagecomponent dies that each include a plurality of data storage circuits,coupled together via an electrical coupling.

FIG. 11 is a schematic representation of a system 1100 that includes acompute fabric die coupled to a sensor die that includes a plurality ofsensors, and coupled to plural storage component dies that each includea plurality of data storage circuits.

FIG. 12 is a schematic representation of a system 1200 that includesplural compute fabric dies, a sensor die that includes a plurality ofsensors, and plural storage component dies that each include a pluralityof data storage circuits, coupled together via an electrical coupling.

FIG. 13 is a schematic representation of a system 1300 in which dies1301 are directly coupled via a through-silicon via (TSV) verticalelectrical connection. In some embodiments, dies 1301 includes at leastone of a compute fabric die, a storage die and a sensor die.

FIG. 14 is a schematic representation of a system 1400 having a stacked2.5D configuration in which dies 1401 are directly coupled via athrough-silicon via (TSV) vertical electrical connection and the dies1401 are coupled to a compute fabric die 1402 via an interposer die 1403that is stacked atop the adjacent die dies 1401 and 1402. In someembodiments, dies 1401 include at least one of a storage die and asensor die.

Roles

In some embodiments, individual data processing components (programmabledata processing circuit component) and data storage components aredirectly and individually programmed for different functions dependingon the roles attributed to the component during program instructionexecution. In some embodiments, each programmable data processingcircuit component is electrically coupled to at least one data storagecircuit component that includes machine-executable program instructionsthat are executable by the programmable data processing circuitcomponent, and wherein each programmable data processing circuitcomponent is programmed by storing program instructions at the storagecircuit component electrically coupled to the data processing circuitcomponent.

Typical roles may include but are not exclusively restricted to “datacollection”, “data integration”, “analysis”, “learning”, “intrinsicproperties”, “profiling”, “monitoring”, “data fusion”, and “dataattestation”.

In some embodiments, in a data collecting role, functions includecommands for enabling and disabling the collection of data from sensorcomponents. Data collecting role functions include commands forconfiguring sensor component operating properties such as sensorsensitivity, dynamic operating range, biasing conditions.

In some embodiments, in a data integration role, functions includealgorithm specific calculations, data retrieval and data storagecommands aimed at combining data captured from sensor components byprocessing and storage components in data collecting roles. Functions inthe data collection role also include commands to configure thefunctionality of components in the data collection role.

In some embodiments, in an analysis role, processing and storageelements perform signal processing or error correction specificcalculations along with associated data retrieval and data storagecommands for preprocessing data in preparation of applying machinelearning techniques. Examples of analysis include data sampling, time orspectral based filtering, recovery of corrupted sensor data. Functionsin the analysis role also include commands to configure thefunctionality of components in the data integration role.

In some embodiments, processing and storage components in the learningrole are programmed with functions for implementing calculations, dataretrieval and storage as defined to the applicable algorithms. Functionsinclude commands for interfacing with components in the analysis role inorder to retrieve data from said components. Functions include commandsto configure the functionality of components in the analysis role.Functions include calculation, data retrieval and data storage commandsnecessary for the implementation of well-known machine learningalgorithms such as statistical classification, statistical spectraldensity estimation, clustering, principal component analysis,independent component analysis, singular value decomposition, learningclassifiers, kernel estimators.

In some embodiments, in the “intrinsic properties” role, processingcomponents execute commands designed to place discrete processing,storage and sensor components in a maintenance mode and where certainbiasing and control parameters of the components in the maintenance modeare continuously changed in order to heuristically collect informationpertinent to the unique intrinsic physical specificities of eachdiscrete component being exercised. These specificities are related tosemiconductor process variations that occur naturally duringmanufacturing.

In some embodiments, the intrinsic physical specificities of discretesensor components are used to calibrate individual sensor components.

In some embodiments, individual intrinsic physical specificities arecombined to calibrate groups of sensor components.

In some embodiments, the intrinsic physical specificities of componentsare applied to security and cryptography applications.

In a profiling role, functions include at least one of capturing andaggregation of statistical heuristic information pertinent to data inorder to generate analytics (characteristic information summaries) forthe purpose of characterizing data quality, detecting and learning datacharacteristic outliers/aberrations, classification of risk modalities,predicting failure probabilities, predicting failure modalities, andlearning/identifying new modalities pertinent to data.

In a monitoring role, functions include comparing data characteristicsagainst expected behavior profiles under defined operating/environmentalparadigms.

In a data fusion role, functions include combining data fromheterogeneous sources/sensors in order to create multi-modal informationby using application/data dependent statistical learning processes. Suchinformation is produced by leveraging machine learning techniques toextract characteristic information from data/sensor sources that rendersinformation properties of interest salient for the purpose of profiling,analysis, analytics extraction, attestation, and the like.

In a data attestation role, functions include at least one of taggingdata and verifying existing embedded data tags in order to verify atleast one of: authenticity (not tampered with), completeness (is anydata missing), traceability (verifiable ledger of hops and/or path hasdata taken before getting here), authentication (source/transmittervalidation and/or recipient validation), authorization (sender/recipientpermission/credentials verification for data transfer), andaccountability (deterministic traceability—is the traceability ledgercorrect/acceptable/match the expected path?).

Data Storage Circuit Components

In some embodiments, at least one data processing circuit component iscoupled to a data storage circuit component that includes processingcircuit instructions for performing linear algebra computation.

In some embodiments, at least one data processing circuit component iscoupled to a data storage circuit component that includes processingcircuit instructions for a statistical classification process.

In some embodiments, at least one data processing circuit component iscoupled to a data storage circuit component that includes processingcircuit instructions for a statistical spectral density estimation.

In some embodiments, at least one data processing circuit component iscoupled to a data storage circuit component that includes processingcircuit instructions for a clustering process.

In some embodiments, at least one data processing circuit component iscoupled to a data storage circuit component that includes processingcircuit instructions for a principal component analysis process.

In some embodiments, at least one data processing circuit component iscoupled to a data storage circuit component that includes processingcircuit instructions for an independent component analysis process.

In some embodiments, at least one data processing circuit component iscoupled to a data storage circuit component that includes processingcircuit instructions for a singular value decomposition process.

In some embodiments, at least one data processing circuit component iscoupled to a data storage circuit component that includes processingcircuit instructions for a learning classifier process.

In some embodiments, at least one data processing circuit component iscoupled to a data storage circuit component that includes processingcircuit instructions for a kernel estimator process.

In some embodiments, at least one data processing circuit component iscoupled to a data storage circuit component that includes processingcircuit instructions for arithmetic computations.

In some embodiments, at least one data processing circuit component iscoupled to a data storage circuit component that includes processingcircuit instructions for selecting a sensor of the microelectronicdevice as a data source, and generating analysis results from datareceived from the selected sensor.

In some embodiments, at least one data processing circuit component iscoupled to a data storage circuit component that includes processingcircuit instructions for selecting at least one of a sensor, a datastorage circuit component, and a data processing circuit components asan intrinsic properties component, generating identifying information bychanging biasing and control parameters of the selected intrinsicproperties component, and generating the identifying information basedon the results of the changing of the biasing and control parameters.

In some embodiments, at least one data processing circuit component iscoupled to a data storage circuit component that includes processingcircuit instructions for tagging analysis results generated for dataprovided by a sensor of the microelectronic device with tagginginformation generated from the identifying information.

In some embodiments, at least one data processing circuit component iscoupled to a data storage circuit component that includes processingcircuit instructions for calibrating at least one of the plurality ofsensors by using the identifying information.

In some embodiments, at least one data processing circuit component iscoupled to a data storage circuit component that includes processingcircuit instructions for generating a secret cryptographic key by usingthe identifying information.

In some embodiments, at least one data processing circuit component iscoupled to a data storage circuit component that includes processingcircuit instructions for generating a cryptographic private/public keypair by using the identifying information.

In some embodiments, at least one data processing circuit component iscoupled to a data storage circuit component that includes processingcircuit instructions for using the identifying information to generate asecret cryptographic key, collecting a first sample of sensor data froma sensor of the microelectronic device, and generating a digitalsignature by signing the first sample of sensor data by using the secretcryptographic key.

In some embodiments, at least one data processing circuit component iscoupled to a data storage circuit component that includes processingcircuit instructions for providing the signature and the first sample ofthe sensor data to a blockchain system.

In some embodiments, at least one data processing circuit component iscoupled to a data storage circuit component that includes processingcircuit instructions for collecting a first sample of sensor data from asensor of the microelectronic device, generating a hash of the firstsample of sensor data, and providing the hash and the first sample ofthe sensor data to an external blockchain system.

In some embodiments, at least one data processing circuit component iscoupled to a data storage circuit component that includes processingcircuit instructions for accessing a public cryptographic key,collecting a first sample of sensor data from a sensor of themicroelectronic device, encrypting the first sample of sensor data byusing the public cryptographic key, and providing the encrypted firstsample of the sensor data to a blockchain system.

In some embodiments, at least one data processing circuit component iscoupled to a data storage circuit component that includes processingcircuit instructions for using the identifying information to generate asecret cryptographic key, collecting a first sample of sensor data froma sensor of the microelectronic device, generating a first datastructure that includes the first sample of sensor data, generating adigital signature by signing the first data structure by using thesecret cryptographic key, and providing the signature and the first datastructure to a blockchain system.

Data Collection Mechanisms and Properties of Hardware Device Embodimentsfor Capturing Information at the Edge (0200) Direct Coupling ThroughIntegrated Sensors

In some embodiments, one or more data collection sensors (e.g., 101 ofFIG. 1, 201 of FIG. 2 ) are integrated into device computation fabric.The sensor or sensors within a device may be of different types, havedifferent function capabilities, data range collection capabilities andoperating ranges.

In some embodiments, at least one sensor of a system (e.g. 100 of FIG.1, 200 of FIG. 2 ) is included in an integrated circuit semiconductordie that includes at least a portion of the compute fabric (e.g., 102 ofFIG. 1, 202 of FIG. 2 ).

In some embodiments, sensor 101 and a runtime-adaptable compute fabric102 are included in a same integrated circuit semiconductor die.

In some embodiments, sensors (e.g., 101, 201) include microelectroniccircuitry constructed to measure absolute voltages, differentialvoltages, direct electric current and alternating electric current. Insome embodiments, sensors (e.g., 101, 201) include at least one ofsensors based on at low-voltage differential signaling (LVDS), andcurrent threshold detectors.

In some embodiments, sensors (e.g., 101, 201) include microelectroniccircuitry constructed to measure electromagnetic waves. In someembodiments, the types and spectral bands that the sensors are capableof sensing depend on the semiconductor properties with which saidmicroelectronic circuitry is implemented. In some embodiments, sensors(e.g., 101, 201) include implementations using High-Electron-MobilityTransistors (HEMT) such as those fabricated in Aluminum Gallium Arsenideon Gallium Arsenide for millimeter-wave sensors integrated withprocessing fabric.

In some embodiments, sensors (e.g., 101, 201) include microelectroniccircuitry constructed to measure magnetic waves. In some embodiments,sensing capabilities depend on the semiconductor properties and designspecifications with which said microelectronic circuitry is implemented.In some embodiments, sensors (e.g., 101, 201) include implementationsusing Gallium Arsenide on Gallium Arsenide for micro-Hall Effect sensorsintegrated with processing fabric.

In some embodiments, sensors (e.g., 101, 201) include microelectroniccircuitry constructed to measure temperature. The sensing capabilitiesdepend on the semiconductor properties and design specifications withwhich said microelectronic circuitry is implemented. In someembodiments, sensors (e.g., 101, 201) include implementations usingGallium Arsenide on Gallium Arsenide for temperature sensors integratedwith processing fabric.

In some embodiments, sensors (e.g., 101, 201) include sensors connectedto a processing layer through indirect optical coupling through anoptical interface layer that is heterogeneously integrated with theprocessing layer. In some embodiments, sensors (e.g., 101, 201) includeimplementations using High-Electron-Mobility Transistors—for instanceIII/V materials such as Indium Gallium Arsenide fabricated photovoltaicbased sensors integrated with processing fabric.

(0300) Direct Coupling Through Integrated Interface Medium (SensorsConnected by Direct Coupling to HI Layer Based Interface)

In some embodiments, one or more data collection sensors (e.g., 101 ofFIG. 1, 201 of FIG. 2 ) are fabricated in a separate semiconductorintegrated circuit die (e.g., 201) from the one containing the devicecompute fabric (e.g., 202). In some embodiments, sensors in the diecontaining the sensor (e.g., 201) are directly coupled to compute fabricin the die containing the device compute fabric (e.g., 202) via aninterface medium.

In some embodiments, at least one sensor in the first sensor die 201 anda first runtime-adaptable compute fabric in the die 202 are directlycoupled via an interface medium.

In some embodiments, the interface medium is a through-silicon via (TSV)vertical electrical connection. In some embodiments, the coupled diesare stacked to form a 3D integrated circuit. In some embodiments, aninterface medium involves a stacked 2.5D configuration were adjacent dieare interconnected using TSVs and an interposer die is stacked atop theadjacent die. In some embodiments, a sensor or sensors within a sensordie may be of different types, have different function capabilities,data range collection capabilities and operating ranges.

In some embodiments, sensors (e.g., 101, 201) include microelectroniccircuitry constructed to measure absolute voltages, differentialvoltages, direct electric current and alternating electric current. Insome embodiments, sensors (e.g., 101, 201) include at lest one ofsensors based on low-voltage differential signaling (LVDS), and currentthreshold detectors.

In some embodiments, sensors (e.g., 101, 201) include microelectroniccircuitry constructed to measure electromagnetic waves. The types andspectral bands that the sensors are capable of sensing depend on thesemiconductor properties with which said microelectronic circuitry isimplemented. In some embodiments, sensors (e.g., 101, 201) includeimplementations using High-Electron-Mobility Transistors (oHEMT) such asthose fabricated in Aluminum Gallium Arsenide on Gallium Arsenide formillimeter-wave sensors.

In some embodiments, sensors (e.g., 101, 201) include microelectroniccircuitry constructed to measure magnetic waves. The sensingcapabilities depend on the semiconductor properties and designspecifications with which said microelectronic circuitry is implemented.In some embodiments, sensors (e.g., 101, 201) include implementationsusing Gallium Arsenide on Gallium Arsenide for micro-Hall Effectsensors.

In some embodiments, sensors (e.g., 101, 201) include microelectroniccircuitry constructed to measure temperature. The sensing capabilitiesdepend on the semiconductor properties and design specifications withwhich said microelectronic circuitry is implemented. In someembodiments, sensors (e.g., 101, 201) include implementations usingGallium Arsenide on Gallium Arsenide for temperature sensors.

In some embodiments, sensors (e.g., 101, 201) include sensors connectedto a processing layer through indirect optical coupling through anoptical interface layer that is heterogeneously integrated with theprocessing layer. In some embodiments, sensors (e.g., 101, 201) includeimplementations using High-Electron-Mobility Transistors—for instanceIII/V materials such as Indium Gallium Arsenide fabricated photovoltaicbased sensors.

(0400) Indirect Coupling Through Integrated Interface Medium (SensorsConnected Through Coupling to HI Layer Based Interface Through One orMore Bridge Device)

FIG. 3 is a schematic representation of a system 300 that is implementedas a microelectronic device that includes at least a first sensor die301, a first runtime-adaptable compute fabric die 302, and a secondsensor die 303. In some embodiments, the first sensor die 301 and thesecond sensor die are similar to the sensor die 201 of FIG. 2 . In someembodiments, the compute fabric die 302 is similar to the compute fabricdie 202 Of FIG. 2 .

In some embodiments, sensors in the die 301 are directly coupled tocompute fabric in the die 302 via a first interface medium and sensorsin the die 303 are directly coupled to the first sensor die 301 via asecond interface medium. In some embodiments, at least one of the firstinterface medium and the second interface medium is a through-siliconvia (TSV) vertical electrical connection. In some embodiments, at leastone pair of coupled dies are stacked to form a 3D integrated circuit. Insome embodiments, at least one of the first interface medium and thesecond interface medium involves a stacked 2.5D configuration wereadjacent die are interconnected using TSVs and an interposer die isstacked atop the adjacent die.

In some embodiments, the compute fabric in the die 302 is constructed toreceive sensor data generated by a sensor in the sensor die 303 via thesensor die 301.

(0500) Indirect Coupling Through Non-Integrated Interface Medium(Sensors Connected To Processing Layer Through a Bridge Device Externalto Device)

FIG. 4 is a schematic representation of a system 400 that is implementedas a microelectronic device that includes at least a first sensor 401, afirst runtime-adaptable compute fabric 402, and a first bridge device403. In some embodiments, the first sensor 401 is similar to the sensor101 and the compute fabric 402 is similar to the compute fabric 102. Insome embodiments, the first sensor 401 is coupled to the first bridgedevice 403, and the first bridge device 403 is coupled to the firstruntime-adaptable compute fabric 402.

In some embodiments, the first runtime-adaptable compute fabric 402 isincluded in a first compute fabric die.

In some embodiments, the compute fabric die includes the first bridgedevice 403.

In some embodiments, the first bridge device 403 is included in a seconddie that is different from the first compute fabric die, and the computefabric die is coupled to the second die via a first integrated interfacemedium.

In some embodiments, the first bridge device 403 is included in a seconddie that is different from the first compute fabric die, and the firstsensor 401 is included in a third die that is different from the firstcompute fabric die and the second die. In some embodiments, the firstsensor 401 is coupled to the first bridge device 403 via a firstintegrated interface medium, as described herein. In some embodiments,the first bridge device 403 is coupled to the compute fabric 402 via asecond integrated interface medium, as described herein.

In some embodiments, at least one of the first interface medium and thesecond interface medium is a through-silicon via (TSV) verticalelectrical connection. In some embodiments, at least one pair of coupleddies is stacked to form a 3D integrated circuit. In some embodiments, atleast one of the first interface medium and the second interface mediuminvolves a stacked 2.5D configuration were adjacent die areinterconnected using TSVs and an interposer die is stacked atop theadjacent die.

In some embodiments, one or more data collection sensors (e.g., 401) areexternal to the first compute fabric die and connected to the computefabric die through the first bridge device 403.

(0600) External Direct-Coupled Sensors

In some embodiments, sensor data processed by a first runtime-adaptablecompute fabric die (e.g., 202 of FIG. 2 ) originates from a secondruntime-adaptable compute fabric die (e.g., 203 of FIG. 3 ) coupled tothe first runtime-adaptable compute fabric die by direct couplingthrough an electric interconnect. In some embodiments, sensor dataprocessed by the first runtime-adaptable compute fabric die originatesfrom a third runtime-adaptable compute fabric die coupled to the firstruntime-adaptable compute fabric die by indirect coupling via anintegrated interface medium, as described herein. In some embodiments,sensor data processed by the first runtime-adaptable compute fabric dieoriginates from a fourth runtime-adaptable compute fabric die coupled tothe first runtime-adaptable compute fabric die by indirect coupling viaa bridge device, as described herein.

In some embodiments, at least two runtime-adaptable compute fabrics areincluded in a same integrated circuit semiconductor die. In someembodiments, the first runtime-adaptable compute fabric and the thirdruntime-adaptable compute fabric are included in different integratedcircuit semiconductor dies, and coupled via an integrated interfacemedium. In some embodiments, the first runtime-adaptable compute fabricand the fourth runtime-adaptable compute fabric are included indifferent integrated circuit semiconductor dies, coupled via a bridgedevice.

In some embodiments, sensor data processed by the firstruntime-adaptable compute fabric die originates from a combination of atleast a second runtime-adaptable compute fabric die directly coupled tothe first runtime-adaptable compute fabric and a third runtime-adaptablecompute fabric die indirectly coupled to the first runtime-adaptablecompute fabric.

(0700) External Indirect-Coupled Sensors

In some embodiments, sensors included in different external devices areindirectly coupled to the compute fabric device.

Sensor Data Transmitter in Fabric Die

In some embodiments, the sensor 101 and the first runtime-adaptablecompute fabric 102 of FIG. 1 are included in an integrated circuitsemiconductor die (first die), and the first die also includes at leasta first transmitter coupled to the sensor 101. In some embodiments, thetransmitter is constructed to transmit sensor data of the sensor 101 toa second sensor that is coupled to a second runtime-adaptable computefabric. In some embodiments, the second runtime-adaptable compute fabricis included in a second die that is different from the first die. Insome embodiments, the first transmitter is a millimeter-wavetransmitter. In some embodiments, the first transmitter is amillimeter-wave transmitter that is coupled to the sensor 101, and thesensor 101 is fabricated using HEMT semiconductor materials. In someembodiments, the first transmitter is a millimeter-wave transmitter thatis coupled to the second sensor, and the second sensor is fabricatedusing HEMT semiconductor materials.

Sensor Data Transmitter in Die Separate from Fabric Die

In some embodiments, the first runtime-adaptable compute fabric of thedie 202 (of FIG. 2 ) is coupled to the sensor die 201 (via one of anintegrated interface medium and a bridge device as described herein) andthe sensor die 201 includes semiconductor materials of a first sensorand at least an integrated first transmitter. In some embodiments, thefirst transmitter of the die 201 is coupled to the sensor of the die201. In some embodiments, the first transmitter of the die 201 isconstructed to transmit sensor data of the sensor of the die 201 to asecond sensor that is coupled to a second runtime-adaptable computefabric. In some embodiments, the second runtime-adaptable compute fabricis included in a second die that is different from the first die 202. Insome embodiments, the first transmitter is a millimeter-wavetransmitter. In some embodiments, the first transmitter is amillimeter-wave transmitter that is coupled to the sensor of the die201, and the sensor of the die 201 is fabricated using HEMTsemiconductor materials. In some embodiments, the first transmitter is amillimeter-wave transmitter that is coupled to the second sensor, andthe second sensor is fabricated using HEMT semiconductor materials.

In some embodiments, a first runtime-adaptable compute fabric isconstructed to process sensor data received from a secondruntime-adaptable compute fabric via at least one of a bridge device, anintegrated interface medium, and a transmitter, as described herein.

(0800) Mixed Coupled Sensors

In some embodiments, a microelectronic device package includes aplurality of a compute fabric dies, each compute fabric die including atleast one compute fabric; wherein at least one compute fabric is coupledto at least one sensor, as described herein; wherein at least a firstcompute fabric of the microelectronic device package is constructed toreceive sensor data via a second compute fabric of the microelectronicdevice package. In some embodiments, the microelectronic device packageincludes a plurality of data collection sensors, each sensor beingcoupled to at least one compute fabric. In some embodiments, theplurality of data collection sensors include at least two sensors thatare different in at least one of type, function capabilities, data rangecollection capabilities and operating ranges. In some embodiments, theplurality of data collection sensors are coupled across one or moredevices within the microelectronic device package by any one of thecircuit coupling arrangements described herein.

FIG. 7

FIG. 7 is a schematic representation of a system 700, according to someembodiments. In some embodiments, the system 700 includes at least onesensor (e.g., 711, 712, 713) and a runtime-adaptable compute fabric. Insome embodiments, the runtime-adaptable compute fabric and the sensorare included in a same microelectronic device.

In some embodiments, the runtime-adaptable compute fabric includes aplurality of compute fabric components, including at least oneprogrammable data processing circuit component (e.g., 722, 732, 742,752, 762, 772, 782, 792) and at least one data storage circuit component(e.g., 723, 733, 743, 753, 763, 773, 783, 793). In some embodiments, thecompute fabric components are arranged on a single compute fabric die(e.g., 703). In some embodiments, the compute fabric components arearranged on a plurality of compute fabric dies. In some embodiments, aprogrammable data processing circuit component (e.g., 722) is coupled toa corresponding data storage circuit component (e.g., 723), and the datastorage circuit component includes instructions (e.g., 724) that areexecuted by the data processing circuit component (e.g., 722). In someembodiments, a programmable data processing circuit component isre-programmed by updating the instructions (e.g., 724, 734, 744, 754,764, 774, 784, 794) stored at the corresponding data storage circuitcomponent (e.g., 723, 733, 743, 753, 763, 773, 783, 793).

In some embodiments, system 700 includes a plurality of sensors 711,712, and 713. In some embodiments, the plurality of sensors and one ormore compute fabric components of the runtime-adaptable compute fabric102 are included in a same microelectronic device package.

In some embodiments, at least one sensor is integrated into theruntime-adaptable compute fabric, wherein the compute fabric includesthe one or more compute fabric components.

In some embodiments, at least one sensor is fabricated in a firstsemiconductor integrated circuit die (e.g., 701), the one or morecompute fabric components are fabricated in a second semiconductorintegrated circuit die (e.g., 703), and at least one sensor of the firstintegrated circuit die is directly coupled to at least one computefabric component of the second semiconductor integrated circuit die viaan interface medium.

In some embodiments, at least one sensor is fabricated in a firstsemiconductor integrated circuit die, the one or more compute fabriccomponents are fabricated in a second semiconductor integrated circuitdie, at least one sensor of the first integrated circuit die is directlycoupled to at least one compute fabric component of the secondsemiconductor integrated circuit die via an interface medium, and asensor external to the microelectronic device is communicatively coupled(or electrically coupled) to a sensor of the first semiconductorintegrated circuit die.

In some embodiments, a sensor is communicatively coupled (orelectrically coupled) to at least one compute fabric component via abridge interface medium that is external to the one or more computefabric component, and the bridge medium is communicatively (orelectrically) coupled to the one or more compute fabric component.

In some embodiments, the system 700 is similar to the system 100. Insome embodiments, the system 700 is similar to the system 200. In someembodiments, the system 700 is similar to the system 300. In someembodiments, the system 700 is similar to the system 400.

In some embodiments, the instructions 724 include instructions forgenerating weighted spatially correlated adjustments for received sensordata.

In some embodiments, the instructions 724 include instructions for adynamic Spline-Laplacian kernel.

In some embodiments, the instructions 724 include instructions forcross-correlating weighted spatially correlated adjustments for receivedsensor data with at least one of: 1) patient characteristic data storedby the microelectronic device; and 2) parameters of anelectroencephalography device.

In some embodiments, the instructions 734 include instructions for atraining model. In some embodiments, the training model is a trainingmodel for cortical and thalamic activity.

In some embodiments, the instructions 744 include instructions for anactive Approximate Entropy (ApEn) learning kernel.

In some embodiments, the instructions 754 include instructions forperforming a weighted Principal Component Analysis process.

In some embodiments, the instructions 764 include instructions for aK-means clustering vector quantization kernel.

In some embodiments, the instructions 774 include instructions for astatistical classification kernel.

In some embodiments, at least one of the instructions 724, 734, 744,754, 764, 774, 784, 794 include instructions for hashing a public key ofa key pair used for encryption.

In some embodiments, at least one of the instructions 724, 734, 744,754, 764, 774, 784, 794 include instructions for decrypting data (e.g.,sensor data, data structures, hashes, and the like).

In some embodiments, the instructions 724, 734, 744, 754, 764, 774, 784,794 and corresponding processing components 722, 732, 742, 752, 762,772, 782, 792 of FIG. 7 are distributed across a plurality of computefabric dies. In some embodiments, each of the processing components 722,732, 742, 752, 762, 772, 782, 792 of FIG. 7 has a same instruction setand architecture. In some embodiments, each of the processing components722, 732, 742, 752, 762, 772, 782, 792 can be reprogrammed by updatingby reprogramming the corresponding instructions. In this manner, processsteps of a method, such as the method described herein with respect toFIG. 6 , can be assigned to specific processing components within amicroelectronic device, and re-assigned to different processingcomponents during run-time by updating the instructions 724, 734, 744,754, 764, 774, 784, 794 during run-time.

3. Methods FIG. 5

FIG. 5 is a representation of a method 500, according to embodiments.

In some embodiments, the method 500 is performed by the system 100 ofFIG. 1 . In some embodiments, the method 500 is performed by the system200 of FIG. 2 . In some embodiments, the method 500 is performed by thesystem 300 of FIG. 3 . In some embodiments, the method 500 is performedby the system 400 of FIG. 4 . In some embodiments, the method 500 isperformed by any one of the systems 700-1400 of FIGS. 7-14 ,respectively.

In some embodiments, the method 500 is performed by a microelectronicdevice that includes: a first sensor die (e.g., 201 of FIG. 2 ) thatincludes a plurality of sensors including a first sensor (e.g., 211); aplurality of runtime-adaptable compute fabric dies (e.g., 202 of FIG. 2) that each comprise a plurality of programmable data processing circuitcomponents (e.g., 222) and data storage circuit components (e.g., 223),wherein within each compute fabric die (e.g., 202) at least one of theprogrammable data processing circuit components (e.g., 222) iselectrically coupled to at least one of the plurality of data storagecircuit components (e.g., 223); and a plurality of storage componentdies (e.g., 231), wherein each storage component die (e.g., 231) iselectrically coupled to at least one of the plurality of compute fabricdies (e.g., 202), wherein the first sensor die (e.g., 201) and eachcompute fabric die (e.g., 202) and storage component die (e.g., 231) isan integrated circuit semiconductor die, wherein the plurality ofcompute fabric dies (e.g., 202) includes at least a first compute fabricdie (e.g., 202) and a second compute fabric die (e.g., 203) electricallycoupled to the first compute fabric die, wherein at least one of a dataprocessing component (e.g., 222) and a storage component (e.g., 223) ofthe microelectronic device is electrically coupled to the first sensor(e.g., 211), wherein each compute fabric die (e.g., 202, 203) has a samesystem architecture, wherein at least one data processing circuitcomponent (e.g., 222) is coupled to a data storage circuit component(e.g., 223) that includes processing circuit instructions (e.g., 224)for selecting at least one sensor (e.g., 211) as a data source, andwherein at least one data processing circuit component (e.g., 222, 232,242, 252) is coupled to a data storage circuit component (e.g., 223,233, 243, 253) that includes processing circuit instructions (e.g., 224,234, 244, 254) for generating analysis results from data received fromthe selected sensor.

As shown in FIG. 5A, the method 500 includes: selecting the first sensoras a data source (process S501); and generating analysis results fromdata received from the selected first sensor (process S502).

In some embodiments, at least one of the first data processing componentand the second data processing component perform the processes S501 toS502. In some embodiments, each of the processes S501 to S502 areperformed by different data processing components of the microelectronicdevice. In some embodiments, instructions for processes S501 to S502 aredistributed across processing components of the microelectronic device.In some embodiments, instructions for processes S501 to S502 aredistributed across processing components of the microelectronic device,and the distribution of processes across the processing components isupdated by the updating program instructions for the processingcomponents stored by respective storage components (e.g., 223).

FIG. 6

FIG. 6A is a representation of a method 600, according to embodiments.

In some embodiments, the method 600 is performed by the system 100 ofFIG. 1 . In some embodiments, the method 600 is performed by the system200 of FIG. 2 . In some embodiments, the method 600 is performed by thesystem 300 of FIG. 3 . In some embodiments, the method 600 is performedby the system 400 of FIG. 4 . In some embodiments, the method 600 isperformed by a microelectronic device similar to the microelectronicdevice described with respect to the method of FIG. 5 .

In some embodiments, the method 600 is performed by any one of thesystems 700-1400 of FIGS. 7-14 , respectively.

As shown in FIG. 6 , the method 600 includes: a first data processingcomponent (Component L) (e.g., 722 of FIG. 7 ) of the microelectronicdevice receiving sensor data provided by at least one sensor component(e.g., 711) of the microelectronic device (process S601). In someembodiments, the sensor data is provided by a first sensor (e.g., 711)of the microelectronic device. In some embodiments, the first sensor iscoupled to EEG probes, and the sensor data is measured electrodepotential differentials reported by the first sensor. In someembodiments, the first data processing component (e.g., 722) producesweighted spatially correlated adjustments for the received sensor data.In some embodiments, the first data processing component (e.g., 722)uses a dynamic Spline-Laplacian kernel to continuously produce weightedspatially correlated adjustments for the received sensor data.

In some embodiments, the method 600 includes: the first data processingcomponent (e.g., 722 of FIG. 7 ) cross-correlating the weightedspatially correlated adjustments for the received sensor data with atleast one of: 1) patient characteristic data stored by themicroelectronic device; and 2) parameters of an electroencephalographydevice (process S602).

In some embodiments, the method 600 includes: the first data processingcomponent (e.g., 722 of FIG. 7 ) using the results of thecross-correlation as a biasing condition for the at least one sensorcomponent (e.g., ₇ 11).

In some embodiments, the method 600 includes: a second data processingcomponent (Component T) (e.g., 732 of FIG. 7 ) of the microelectronicdevice receiving a first datum of the received sensor data andprocessing the first datum by using a training model to generate firstanalysis results (process S603). In some embodiments, the training modelis a training model for cortical and thalamic activity. In someembodiments, the first datum includes sensor data adjusted by weightedspatially correlated adjustments generated by the first data processingcomponent (e.g., 722). In some embodiments, the second data processingcomponent (e.g., 732) receives the first datum from the first dataprocessing component (e.g., 722).

In some embodiments, the method 600 includes: a third data processingcomponent (Component E) (e.g., 742 of FIG. 7 ) of the microelectronicdevice receiving the first datum of the received sensor data from thefirst data processing component, and extracting features from the firstdatum by using an active Approximate Entropy (ApEn) learning kernel(process S604).

In some embodiments, the method 600 includes: the third data processingcomponent (Component E) (e.g., 742 of FIG. 7 ) receiving the firstanalysis results from the second data processing component, andextracting features from the first analysis results by using an activeApproximate Entropy (ApEn) learning kernel (process S605).

In some embodiments, the method 600 includes: a fourth data processingcomponent (Component P) (e.g., 752 of FIG. 7 ) of the microelectronicdevice receiving features extracted by the third data processingcomponent, and detersmining discrete cluster centroid spaces (forK-means clustering) by performing a weighted Principal ComponentAnalysis process (process S606).

In some embodiments, the method 600 includes: a fifth data processingcomponent (Component K) (e.g., 762 of FIG. 7 ) of the microelectronicdevice receiving information indicating the discrete cluster centroidspaces from the fourth data processing component, and using a K-meansclustering vector quantization kernel to produce real-timepatient-specific epilepsy risk classifier information from theinformation indicating the discrete cluster centroid spaces (processS607).

In some embodiments, the method 600 includes: a sixth data processingcomponent (Component M) (e.g., 772 of FIG. 7 ) of the microelectronicdevice actively monitoring the real-time patient-specific epilepsy riskclassifier information generated by the fifth data processing componentby using a statistical classification kernel to generate and store anepilepsy diagnosis (process S608).

In some embodiments, the method 600 includes: the sixth data processingcomponent (Component M) (e.g., 772 of FIG. 7 ) of the microelectronicdevice providing generated epilepsy diagnosis information to hardwaredevice that is external to the microelectronic device (process S609).

4. Machines

The systems and methods of some embodiments and variations thereof canbe embodied and/or implemented at least in part as a machine configuredto receive a computer-readable medium storing computer-readableinstructions. The instructions are preferably executed bycomputer-executable components. The computer-readable medium can bestored on any suitable computer-readable media such as RAMs, ROMs, flashmemory, EEPROMs, optical devices (CD or DVD), hard drives, floppydrives, or any suitable device. The computer-executable component ispreferably a general or application specific processor, but any suitablededicated hardware or hardware/firmware combination device canalternatively or additionally execute the instructions.

5. Conclusion

As a person skilled in the art will recognize from the previous detaileddescription and from the figures and claims, modifications and changescan be made to the embodiments disclosed herein without departing fromthe scope defined in the claims.

What is claimed is:
 1. A microelectronic device comprising: a firstsensor die that includes at least a first sensor that is constructed togenerate electromagnetic measurement data; and at least oneruntime-adaptable compute fabric die comprising: at least one computefabric component that is electrically coupled to the first sensor, andprocessing circuit instructions for generating analysis results fromdata received from the first sensor.
 2. A microelectronic devicecomprising: a first sensor die that includes at least a first sensorthat is constructed to generate magnetic wave measurement data; and atleast one runtime-adaptable compute fabric die comprising: at least onecompute fabric component that is electrically coupled to the firstsensor, and processing circuit instructions for generating analysisresults from data received from the first sensor.